Robust Sliding Mode and Admittance Control of Underactuated Aerial Manipulators for Contact-Based Inspection

• URL: http://arxiv.org/abs/2608.10656v1
• Date: Tue, 11 Aug 2026 08:39:05 GMT
• Title: Robust Sliding Mode and Admittance Control of Underactuated Aerial Manipulators for Contact-Based Inspection
• Abstract summary: Underactuated aerial manipulators present control challenges due to dynamic coupling between vehicle attitude and force generation.<n>This paper proposes a robust control framework for an underactuated hexarotor equipped with a 1-DoF manipulator to perform sustained contact inspection.
• Score: 1.3199511198128897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contact-based industrial inspection requires aerial platforms to maintain stable interaction while rejecting disturbances. Underactuated aerial manipulators present control challenges due to the dynamic coupling between vehicle attitude and force generation. This paper proposes a robust control framework for an underactuated hexarotor equipped with a 1-DoF manipulator to perform sustained contact inspection. The architecture integrates integral-augmented Sliding Mode Control (SMC) for trajectory tracking with an admittance control law for force regulation. The contact force is mapped to a feedforward attitude term, while the 1-DoF arm actively compensates for the tilt to maintain surface alignment. Software-in-the-loop simulations demonstrate that the SMC-based approach achieves superior tracking and coupling rejection compared to traditional PID. Furthermore, the interaction strategy achieved precise force regulation with an RMSE of 0.12N and was able to stably exert up to 20N force, confirming the system's efficacy for stable, reliable contact-based inspection.

Related papers

• Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation [14.501012360823886]
  Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion.<n>This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation.
  arXiv  Detail & Related papers  (2026-09-12T07:46:53Z)
• Contact-Aware Incremental Model Predictive Control for an Underactuated Aerial Manipulator [0.9328991021103293]
  We present a robust contact-aware control framework for aerial writing on an underactuated platform.<n>The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm.
  arXiv  Detail & Related papers  (2026-09-10T15:03:02Z)
• Impedance Control of Ship-Borne Manipulators via Optimization-based Task-Space Inverse Dynamics [23.54472533643259]
  Ship-borne manipulators are subject to wave-induced base motions that introduce disturbances and dynamic coupling.<n>This paper proposes a torque-level optimization-based control framework that integrates high-precision trajectory tracking with task-space impedance for ship-borne manipulators.
  arXiv  Detail & Related papers  (2026-07-24T06:54:13Z)
• Safety-Critical Adaptive Impedance Control via Nonsmooth Control Barrier Functions under State and Input Constraints [3.1685575193428845]
  We propose an online adaptive impedance control framework that enforces joint-state safety while achieving compliant interaction under uncertain dynamics.<n>The approach combines a quadratic-program-based safety filter with a novel composed position-velocity non-smooth control barrier function.<n>A disturbance-observer-enhanced safety mechanism improves robustness against modelling errors and external interaction forces.
  arXiv  Detail & Related papers  (2026-05-27T12:04:03Z)
• ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching [53.30290192030814]
  ForceFlow is a force-aware reactive framework built upon flow matching.<n>We show that ForceFlow achieves a 37% success rate improvement over the strong baseline ForceVLA.<n>It also exhibits accurate force signal prediction and superior performance in contact force self-regulation.
  arXiv  Detail & Related papers  (2026-05-11T13:27:00Z)
• A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance [45.63674336069651]
  Soft continuum manipulators provide passive safety through mechanical compliance.<n>Existing approaches, such as sampling-based planning, are often computationally expensive and lack formal safety guarantees.<n>This paper presents a closed-form Control Lyapunov Function--Control Barrier Function (CLF--CBF) controller for real-time 3D obstacle avoidance.
  arXiv  Detail & Related papers  (2026-03-19T19:34:49Z)
• ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation [75.39847750457626]
  ForceVLA2, an end-to-end vision-language-action framework, equips robots with hybrid force-position control and explicit force awareness.<n>To support learning and evaluation, we construct ForceVLA2-Dataset, containing 1,000 trajectories over 5 contact-rich tasks.<n>Experiments show that ForceVLA2 substantially improves success rates and reliability in contact-rich manipulation.
  arXiv  Detail & Related papers  (2026-03-16T12:03:58Z)
• Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control [46.47619556874003]
  We propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning policy for legged locomotion.<n>The admittance controller maps external wrenches--such as those applied by a human during physical interaction--into desired end-effector velocities, allowing for compliant behavior.<n>We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor.
  arXiv  Detail & Related papers  (2026-03-02T22:31:12Z)
• Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation [55.969741720077565]
  Contact-rich manipulation demands human-like integration of perception and force feedback.<n>Existing learning-based policies often entangle these roles in a monolithic network.<n>We propose a global-local vision-force policy in which a global policy guides free-space actions using vision, and upon contact, a high-frequency local policy with force feedback estimates the interaction frame and executes hybrid force-position control for stable interaction.
  arXiv  Detail & Related papers  (2026-02-25T16:35:24Z)
• Learning Agile and Robust Omnidirectional Aerial Motion on Overactuated Tiltable-Quadrotors [15.511018281264674]
  This work investigates reinforcement learning for omnidirectional aerial motion control on over-actuated tiltable quadrotors.<n>We present a learning-based control framework that enables efficient acquisition of coordinated rotor-joint behaviors for reaching target poses.
  arXiv  Detail & Related papers  (2026-02-25T05:13:25Z)
• Aerial Manipulation with Contact-Aware Onboard Perception and Hybrid Control [11.342874824540493]
  Aerial manipulation (AM) promises to move Unmanned Aerial Vehicles (UAVs) beyond passive inspection to contact-rich tasks such as grasping, assembly, and in-situ maintenance.<n>We present a fully onboard perception-control pipeline for contact-rich AM that achieves accurate motion tracking and regulated contact wrenches without external motion capture (MoCap)<n> Experiments show that our approach closes the perception-to-wrench loop using only onboard sensing, yielding a velocity estimation improvement of 66.01% at contact, reliable target approach, and stable force holding-pointing toward deployable, in-the-wild aerial manipulation.
  arXiv  Detail & Related papers  (2026-02-09T04:10:39Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.